Articles | Volume 22, issue 2
Research article
07 Apr 2015
Research article |  | 07 Apr 2015

Improved variational methods in statistical data assimilation

J. Ye, N. Kadakia, P. J. Rozdeba, H. D. I. Abarbanel, and J. C. Quinn

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Cited articles

Abarbanel, H. D.: Predicting the Future: Completing Models of Observed Complex Systems, Springer, New York, 2013.
Aguiar e Oliviera, H., Ingber, L., Petraglia, A., Petraglia, M. R., and Machado, M. A. S.: Stochastic Global Optimization and Its Applications with Fuzzy Adaptive Simulated Annealing, Vol. 35, Springer, New York, 2012.
Bennett, A. F.: Inverse Modeling of the Ocean and Atmosphere, Cambridge University Press, 2002.
Eibern, H. and Schmidt, H.: A four-dimensional variational chemistry data assimilation scheme for Eulerian chemistry transport modeling, J. Geophys. Res.-Atmos., 104, 18583–18598, 1999.
Evensen, G.: Data Assimilation: The Ensemble Kalman Filter, Springer, New York, 2009.
Short summary
We propose an improved method of data assimilation, in which measured data are incorporated into a physically based model. In data assimilation, one typically seeks to minimize some cost function; here, we discuss a variational approximation in which model and measurement errors are Gaussian, combined with an annealing method, to consistently identify a global minimum of this cost function. We illustrate this procedure with archetypal chaotic systems, and discuss higher-order corrections.